Llama-3_3-Nemotron-Super-49B-v1_5
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
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Deep dive
Notes, sources, and the full write-up
Llama-3_3-Nemotron-Super-49B-v1_5
Llama-3_3-Nemotron-Super-49B-v1_5 is a 49.9B-parameter other model from nvidia. At Q4_K_M it needs roughly 29 GB of VRAM, placing it in the 24-48gb hardware tier.
Specifications
| Spec | Value |
|---|---|
| Parameters | 49.9B |
| Context length | 131K tokens |
| License | other |
| Modalities | text |
| Released | 2025-07-25 |
| Weights | nvidia/Llama-3_3-Nemotron-Super-49B-v1_5 |
VRAM requirements
| Quant | VRAM | Runs on |
|---|---|---|
| Q4_K_M | ~29 GB | RTX 6000 Ada, dual RTX 3090 |
| Q5_K_M | ~35 GB | RTX 6000 Ada, dual RTX 3090 |
| Q8_0 | ~53 GB | A100 80GB, H100 |
| FP16 | ~100 GB | multi-GPU / datacenter |
VRAM is estimated from parameter count; MoE models still need all weights resident.
How to run
vLLM:
vllm serve nvidia/Llama-3_3-Nemotron-Super-49B-v1_5Popularity
Llama-3_3-Nemotron-Super-49B-v1_5 has 523,832 downloads in the last month on HuggingFace and 233 likes.
Frequently asked
Quick answers to common questions
How much VRAM does Llama-3_3-Nemotron-Super-49B-v1_5 need?
Llama-3_3-Nemotron-Super-49B-v1_5 with 49.9B parameters needs approximately 29 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is Llama-3_3-Nemotron-Super-49B-v1_5 better than other nvidia models?
Llama-3_3-Nemotron-Super-49B-v1_5 has 49.9B parameters with 131,072 context - a strong choice for general use.
What license is Llama-3_3-Nemotron-Super-49B-v1_5 under?
Llama-3_3-Nemotron-Super-49B-v1_5 is released under the other license, making it suitable for most commercial and personal projects.
What hardware runs Llama-3_3-Nemotron-Super-49B-v1_5 well?
With 49.9B parameters, Llama-3_3-Nemotron-Super-49B-v1_5 requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for Llama-3_3-Nemotron-Super-49B-v1_5?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~35 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
How long can Llama-3_3-Nemotron-Super-49B-v1_5's context window handle?
Llama-3_3-Nemotron-Super-49B-v1_5 supports a 131,072-token context window - enough for very long documents, codebases, or multi-turn conversations. Real-world usable context may vary by implementation.
What models compete with Llama-3_3-Nemotron-Super-49B-v1_5?
Llama-3_3-Nemotron-Super-49B-v1_5 competes with other 25B–75B. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec
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